Sven Dorkenwald

The paper presents tools to reconstruct spinal cord circuits and identifies modality-specific inhibitory synapses between sensory neurons and inhibitory neurons.

The paper uses a Drosophila connectome and biophysical simulations to show that a reciprocal circuit between T4a motion detectors and vCH cells supports figure-ground discrimination through shunting inhibition.

The paper introduces a plasticity-switching recurrent neural network (psRNN) that models synaptic state-dependent plasticity and shows it improves learning efficiency in cognitive tasks.

The paper maps synapse types across the Drosophila brain connectome, revealing structured patterns of mixed polarity and reciprocal connectivity.

The paper presents the first densely reconstructed adult fruit fly connectome integrating the brain and ventral nerve cord, revealing distributed control circuits involving local and long-range neural connections.

The paper presents a computational pipeline for classifying postsynaptic targets in neuronal meshes, achieving high accuracy on mouse visual cortex data and demonstrating generalizability to other connectomes.

The paper improves synapse detection in Drosophila brain connectomes using new neural networks, achieving better performance in challenging regions and improved neuron clustering.

The paper identifies and analyzes sexually dimorphic neurons in the Drosophila brain using connectome data to understand their connectivity and functional roles in behavior.

CAVE enables reproducible connectome analysis in large-scale datasets during ongoing annotation and proofreading.

NEURD automates proofreading and feature extraction from connectomics data to improve accuracy and accessibility of neural morphology analyses.

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

The paper uses graph-based machine learning to analyze dendritic morphology of excitatory neurons in mouse visual cortex, finding a continuous morphological landscape rather than discrete types.

The paper presents the first neuronal wiring diagram of a whole adult Drosophila brain, revealing synaptic connections and circuit mechanisms underlying sensorimotor behaviors.

The paper creates a computational model of the Drosophila brain connectome to study sensorimotor processing and validates predictions through experiments.

The paper completes the list of cell types in the Drosophila optic lobe and identifies connectivity rules, revealing new families and clusters related to visual functions.

The paper analyzes the network properties of the Drosophila whole-brain connectome, identifying rich club organization and signal integration/broadcasting neurons.

The paper reconstructs and analyzes the female Drosophila ventral nerve cord connectome, identifying neural circuits that coordinate wings and legs during escape takeoff.

The paper analyzes premotor circuits in Drosophila to compare leg and wing motor control networks, finding modular organization and differences in synaptic connectivity.

The paper provides a connectomic description of ascending and descending neurons in the Drosophila nervous system, revealing sexual dimorphism and motor-related circuits.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

The paper uses artificial neural networks to classify neurotransmitters from electron microscopy images of Drosophila melanogaster synapses, achieving high accuracy and revealing ultrastructural differences.

SegCLR, a self-supervised learning method, enables classification of cellular subcompartments and cell types from 3D electron microscopy data with minimal labeled data.

The paper introduces a method to learn a causal model of the fly brain using a connectome and optogenetic perturbation data, revealing that global brain dynamics arise from small, localized circuits.

The paper investigates the synaptic connectivity of thick tufted layer 5 pyramidal neurons in the mouse visual cortex, revealing their local inhibitory and long-range excitatory connections.

Annotating the 127,978-neuron FlyWire fly connectome yields 4,552 cell types, 3,094 cross-validated against the hemibrain; cell-type edges above 10 synapses or 1.1% of input recur over 90% across hemispheres.

The paper predicts MET-type identities from EM data and shows they have distinct connectivity patterns and synapse targets.

Electron microscopy of 1352 neurons in a mouse visual cortex column reveals widespread cell-type-specific inhibition, a basket-cell-targeting disinhibitory class, and interneuron motif groups sharing excitatory targets.

The paper maps synaptic connectivity between cortical pyramidal neurons and shows synapse size is modeled by a combination of binary and analog variables.

The paper uses electron microscopy data to classify cell types in the neocortex based on perisomatic features, showing that these features are sufficient for accurate classification even in large datasets.